Evolutionary self-organization of an artificial potential field map with a group of autonomous robots

Jiming Liu; J. B. Wu; David A. Maluf

doi:10.1109/CEC.1999.781946

Evolutionary self-organization of an artificial potential field map with a group of autonomous robots

Jiming Liu, J. B. Wu, David A. Maluf

Department of Computer Science

Research output: Contribution to conference › Conference paper › peer-review

1 Citation (Scopus)

Abstract

This paper is concerned with two issues: (1) how to enable distributed robots to dynamically acquire their goal-directed collective behaviors, and (2) how to apply the methodology of collective behavioral learning to solve the world modeling problems in mobile robot navigation. We have developed an evolutionary self-organization approach to collective task handling, and furthermore, demonstrated the implemented approach in tackling the specific problem of collectively constructing a global spatial representation, i.e., an artificial potential field map, in an unknown environment.

Original language	English
Pages	350-357
Number of pages	8
DOIs	https://doi.org/10.1109/CEC.1999.781946
Publication status	Published - 1999
Event	1999 Congress on Evolutionary Computation, CEC 1999 - Washington, DC, United States Duration: 6 Jul 1999 → 9 Jul 1999

Conference

Conference	1999 Congress on Evolutionary Computation, CEC 1999
Country/Territory	United States
City	Washington, DC
Period	6/07/99 → 9/07/99

Scopus Subject Areas

Computational Mathematics

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10.1109/CEC.1999.781946

Cite this

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title = "Evolutionary self-organization of an artificial potential field map with a group of autonomous robots",

abstract = "This paper is concerned with two issues: (1) how to enable distributed robots to dynamically acquire their goal-directed collective behaviors, and (2) how to apply the methodology of collective behavioral learning to solve the world modeling problems in mobile robot navigation. We have developed an evolutionary self-organization approach to collective task handling, and furthermore, demonstrated the implemented approach in tackling the specific problem of collectively constructing a global spatial representation, i.e., an artificial potential field map, in an unknown environment.",

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N2 - This paper is concerned with two issues: (1) how to enable distributed robots to dynamically acquire their goal-directed collective behaviors, and (2) how to apply the methodology of collective behavioral learning to solve the world modeling problems in mobile robot navigation. We have developed an evolutionary self-organization approach to collective task handling, and furthermore, demonstrated the implemented approach in tackling the specific problem of collectively constructing a global spatial representation, i.e., an artificial potential field map, in an unknown environment.

AB - This paper is concerned with two issues: (1) how to enable distributed robots to dynamically acquire their goal-directed collective behaviors, and (2) how to apply the methodology of collective behavioral learning to solve the world modeling problems in mobile robot navigation. We have developed an evolutionary self-organization approach to collective task handling, and furthermore, demonstrated the implemented approach in tackling the specific problem of collectively constructing a global spatial representation, i.e., an artificial potential field map, in an unknown environment.

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U2 - 10.1109/CEC.1999.781946

DO - 10.1109/CEC.1999.781946

M3 - Conference paper

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EP - 357

T2 - 1999 Congress on Evolutionary Computation, CEC 1999

Y2 - 6 July 1999 through 9 July 1999

ER -

Evolutionary self-organization of an artificial potential field map with a group of autonomous robots

Abstract

Conference

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